transformers/tests/models/zoedepth/test_image_processing_zoedepth.py

# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import unittest
from dataclasses import dataclass

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from transformers import ZoeDepthImageProcessor

    if is_torchvision_available():
        from transformers import ZoeDepthImageProcessorFast


@dataclass
class ZoeDepthDepthOutputProxy:
    predicted_depth: torch.FloatTensor = None


class ZoeDepthImageProcessingTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        ensure_multiple_of=32,
        keep_aspect_ratio=False,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        do_pad=True,
    ):
        size = size if size is not None else {"height": 18, "width": 18}
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = size
        self.ensure_multiple_of = ensure_multiple_of
        self.keep_aspect_ratio = keep_aspect_ratio
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_pad = do_pad

    def prepare_image_processor_dict(self):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "ensure_multiple_of": self.ensure_multiple_of,
            "keep_aspect_ratio": self.keep_aspect_ratio,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }

    def expected_output_image_shape(self, images):
        return self.num_channels, self.ensure_multiple_of, self.ensure_multiple_of

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )

    def prepare_depth_outputs(self):
        depth_tensors = prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=1,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=True,
            torchify=True,
        )
        depth_tensors = [depth_tensor.squeeze(0) for depth_tensor in depth_tensors]
        stacked_depth_tensors = torch.stack(depth_tensors, dim=0)
        return ZoeDepthDepthOutputProxy(predicted_depth=stacked_depth_tensors)


@require_torch
@require_vision
class ZoeDepthImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = ZoeDepthImageProcessor if is_vision_available() else None
    fast_image_processing_class = ZoeDepthImageProcessorFast if is_torchvision_available() else None

    def setUp(self):
        super().setUp()

        self.image_processor_tester = ZoeDepthImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_processor_properties(self):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processing, "image_mean"))
        self.assertTrue(hasattr(image_processing, "image_std"))
        self.assertTrue(hasattr(image_processing, "do_normalize"))
        self.assertTrue(hasattr(image_processing, "do_resize"))
        self.assertTrue(hasattr(image_processing, "size"))
        self.assertTrue(hasattr(image_processing, "ensure_multiple_of"))
        self.assertTrue(hasattr(image_processing, "do_rescale"))
        self.assertTrue(hasattr(image_processing, "rescale_factor"))
        self.assertTrue(hasattr(image_processing, "do_pad"))

    def test_image_processor_from_dict_with_kwargs(self):
        for image_processing_class in self.image_processor_list:
            image_processor = image_processing_class(**self.image_processor_dict)
            self.assertEqual(image_processor.size, {"height": 18, "width": 18})

        for image_processing_class in self.image_processor_list:
            modified_dict = self.image_processor_dict
            modified_dict["size"] = 42
            image_processor = image_processing_class(**modified_dict)
            self.assertEqual(image_processor.size, {"height": 42, "width": 42})

    def test_ensure_multiple_of(self):
        # Test variable by turning off all other variables which affect the size, size which is not multiple of 32
        image = np.zeros((489, 640, 3))

        size = {"height": 380, "width": 513}
        multiple = 32
        for image_processor_class in self.image_processor_list:
            image_processor = image_processor_class(
                do_pad=False, ensure_multiple_of=multiple, size=size, keep_aspect_ratio=False
            )
            pixel_values = image_processor(image, return_tensors="pt").pixel_values

            self.assertEqual(list(pixel_values.shape), [1, 3, 384, 512])
            self.assertTrue(pixel_values.shape[2] % multiple == 0)
            self.assertTrue(pixel_values.shape[3] % multiple == 0)

        # Test variable by turning off all other variables which affect the size, size which is already multiple of 32
        image = np.zeros((511, 511, 3))

        height, width = 512, 512
        size = {"height": height, "width": width}
        multiple = 32
        for image_processor_class in self.image_processor_list:
            image_processor = image_processor_class(
                do_pad=False, ensure_multiple_of=multiple, size=size, keep_aspect_ratio=False
            )
            pixel_values = image_processor(image, return_tensors="pt").pixel_values

            self.assertEqual(list(pixel_values.shape), [1, 3, height, width])
            self.assertTrue(pixel_values.shape[2] % multiple == 0)
            self.assertTrue(pixel_values.shape[3] % multiple == 0)

    def test_keep_aspect_ratio(self):
        # Test `keep_aspect_ratio=True` by turning off all other variables which affect the size
        height, width = 489, 640
        image = np.zeros((height, width, 3))

        size = {"height": 512, "width": 512}
        for image_processor_class in self.image_processor_list:
            image_processor = image_processor_class(
                do_pad=False, keep_aspect_ratio=True, size=size, ensure_multiple_of=1
            )
            pixel_values = image_processor(image, return_tensors="pt").pixel_values

            # As can be seen, the image is resized to the maximum size that fits in the specified size
            self.assertEqual(list(pixel_values.shape), [1, 3, 512, 670])

        # Test `keep_aspect_ratio=False` by turning off all other variables which affect the size
        for image_processor_class in self.image_processor_list:
            image_processor = image_processor_class(
                do_pad=False, keep_aspect_ratio=False, size=size, ensure_multiple_of=1
            )
            pixel_values = image_processor(image, return_tensors="pt").pixel_values

            # As can be seen, the size is respected
            self.assertEqual(list(pixel_values.shape), [1, 3, size["height"], size["width"]])

        # Test `keep_aspect_ratio=True` with `ensure_multiple_of` set
        image = np.zeros((489, 640, 3))

        size = {"height": 511, "width": 511}
        multiple = 32
        for image_processor_class in self.image_processor_list:
            image_processor = image_processor_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=multiple)

            pixel_values = image_processor(image, return_tensors="pt").pixel_values

            self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])
            self.assertTrue(pixel_values.shape[2] % multiple == 0)
            self.assertTrue(pixel_values.shape[3] % multiple == 0)

    # extend this test to check if removal of padding works fine!
    def test_post_processing_equivalence(self):
        outputs = self.image_processor_tester.prepare_depth_outputs()
        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
        image_processor_slow = self.image_processing_class(**self.image_processor_dict)

        source_sizes = [outputs.predicted_depth.shape[1:]] * self.image_processor_tester.batch_size
        target_sizes = [
            torch.Size([outputs.predicted_depth.shape[1] // 2, *(outputs.predicted_depth.shape[2:])])
        ] * self.image_processor_tester.batch_size

        processed_fast = image_processor_fast.post_process_depth_estimation(
            outputs,
            source_sizes=source_sizes,
            target_sizes=target_sizes,
        )
        processed_slow = image_processor_slow.post_process_depth_estimation(
            outputs,
            source_sizes=source_sizes,
            target_sizes=target_sizes,
        )
        for pred_fast, pred_slow in zip(processed_fast, processed_slow):
            depth_fast = pred_fast["predicted_depth"]
            depth_slow = pred_slow["predicted_depth"]

            torch.testing.assert_close(depth_fast, depth_slow, atol=1e-1, rtol=1e-3)
            self.assertLessEqual(torch.mean(torch.abs(depth_fast.float() - depth_slow.float())).item(), 5e-3)